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Smart O.,Emory University | Smart O.,Georgia Institute of Technology | Tsoulos I.G.,Technological Educational Institute TEI of Epirus | Gavrilis D.,Athena Research Center | Georgoulas G.,Technological Educational Institute TEI of Epirus
Expert Systems with Applications | Year: 2011

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (∼65-95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors. © 2011 Published by Elsevier Ltd.

Kardara M.,National Technical University of Athens | Papadakis G.,Athena Research Center | Papaoikonomou A.,National Technical University of Athens | Tserpes K.,National Technical University of Athens | Varvarigou T.,National Technical University of Athens
Information Processing and Management | Year: 2015

Influence theories constitute formal models that identify those individuals that are able to affect and guide their peers through their activity. There is a large body of work on developing such theories, as they have important applications in viral marketing, recommendations, as well as information retrieval. Influence theories are typically evaluated through a manual process that cannot scale to data voluminous enough to draw safe, representative conclusions. To overcome this issue, we introduce in this paper a formalized framework for large-scale, automatic evaluation of topic-specific influence theories that are specialized in Twitter. Basically, it consists of five conjunctive conditions that are indicative of real influence exertion: the first three determine which influence theories are compatible with our framework, while the other two estimate their relative effectiveness. At the core of these two conditions lies a novel metric that assesses the aggregate sentiment of a group of users and allows for estimating how close the behavior of influencers is to that of the entire community. We put our framework into practice using a large-scale test-bed with real data from 75 Twitter communities. In order to select the theories that can be employed in our analysis, we introduce a generic, two-dimensional taxonomy that elucidates their functionality. With its help, we ended up with five established topic-specific theories that are applicable to our settings. The outcomes of our analysis reveal significant differences in their performance. To explain them, we introduce a novel methodology for delving into the internal dynamics of the groups of influencers they define. We use it to analyze the implications of the selected theories and, based on the resulting evidence, we propose a novel partition of influence theories in three major categories with divergent performance. © 2014 Elsevier Ltd. All rights reserved.

Goasdoue F.,University of Rennes 1 | Goasdoue F.,French Institute for Research in Computer Science and Automation | Kaoudi Z.,Athena Research Center | Manolescu I.,French Institute for Research in Computer Science and Automation | And 2 more authors.
Proceedings - International Conference on Data Engineering | Year: 2015

As increasing volumes of RDF data are being produced and analyzed, many massively distributed architectures have been proposed for storing and querying this data. These architectures are characterized first, by their RDF partitioning and storage method, and second, by their approach for distributed query optimization, i.e., determining which operations to execute on each node in order to compute the query answers. We present CliqueSquare, a novel optimization approach for evaluating conjunctive RDF queries in a massively parallel environment. We focus on reducing query response time, and thus seek to build flat plans, where the number of joins encountered on a root-to-leaf path in the plan is minimized. We present a family of optimization algorithms, relying on n-ary (star) equality joins to build flat plans, and compare their ability to find the flattest possibles. We have deployed our algorithms in a MapReduce-based RDF platform and demonstrate experimentally the interest of the flat plans built by our best algorithms. © 2015 IEEE.

Djahandideh B.,French Institute for Research in Computer Science and Automation | Goasdoue F.,University of Rennes 1 | Kaoudi Z.,Athena Research Center | Manolescu I.,French Institute for Research in Computer Science and Automation | And 2 more authors.
Proceedings - International Conference on Data Engineering | Year: 2015

RDF is an increasingly popular data model for many practical applications, leading to large volumes of RDF data; efficient RDF data management methods are crucial to allow applications to scale. We propose to demonstrate CliqueSquare, an RDF data management system built on top of a MapReduce-like infrastructure. The main technical novelty of CliqueSquare resides in its logical query optimization algorithm, guaranteed to find a logical plan as flat as possible for a given query, meaning: a plan having the smallest possible number of join operators on top of each other. CliqueSquare's ability to build flat plans allows it to take advantage of a parallel processing framework in order to shorten response times. We demonstrate loading and querying the data, with a particular focus on query optimization, and on the performance benefits of CliqueSquare's flat plans. © 2015 IEEE.

Kaoudi Z.,Athena Research Center | Kementsietsidis A.,Google
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

RDF has become recently a very popular data model used in a variety of applications and use cases in both academia and industry. Query processing and evaluation is a central component in data management in general and is, thus, unsurprisingly one of the most active areas of research in the field of RDF data management. In this chapter we provide an overview of query processing techniques for the RDF data model using different system architectures. We survey techniques for both centralized and distributed RDF stores, including peer-to-peer, federated and cloud-based systems. © Springer International Publishing Switzerland 2014.

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